A conventional logistics simulation system merely virtually triggers a discrete event according to related production data or a customized correlated condition, and analyzes possible or potential data in real production by virtually running, so as to resolve most evaluation problems and bottleneck problems according to previous rules or empirical values. However, relatively large uncertainty exists in a relationship between an analysis result and actual running because a production condition changes dramatically and the simulation system is totally independent of a real-time production condition.
Many logistics management (scheduling) systems can collect real-time data only after being totally jointed with reality, and calculate an actual scheduling manner of production by using the real-time data. Therefore, many evaluation mechanisms and bottleneck problems cannot be prevented in advance, and optimization and improvement can be performed only after the systems run for some time, thereby causing increasing investment of human and material resources.
Some enterprises first use logistics simulation software to perform optimization with respect to production-related problems, and then control a logistics system by using a real-time scheduling system. It seems that the problems in the foregoing two aspects are resolved. However, a related algorithm and a trigger condition of simulation software cannot totally match a related algorithm and a trigger condition of an actual control system, and differences therebetween cause that the two are independent of each other and an expected objective cannot be achieved.